Customer Success Metrics Priority Map
Every customer success metric in one view, with its priority at each maturity stage. See what to focus on now and what comes next.
Focus on GRR first. You need to stop the bleeding before optimizing for growth. NRR is noise when your churn fundamentals aren't solid.
Start tracking NRR alongside GRR. Use it to build the case for CS as a revenue function, but don't optimize for it yet.
NRR should be a primary CS metric. Your expansion motions and risk management should directly influence this number.
This is your most important metric. If GRR is low, nothing else matters; you're losing the foundation.
GRR remains critical. Your playbooks and health scores should be directly aimed at improving this number.
GRR should be stable and high. Now focus on understanding GRR by cohort and segment to find hidden weaknesses.
Track this alongside GRR. It's the simplest retention metric and tells you the raw reality of how many customers are leaving.
Segment logo retention by tier and cohort. Patterns here drive your risk management playbooks.
Logo retention should be stable. Focus on understanding which segments and cohorts deviate and why.
Start by defining what 'value' means for your product. Even an informal TTV target is better than nothing.
Define TTV formally by segment. This becomes the north star for your onboarding playbook.
TTV should be tracked, benchmarked, and actively optimized. Correlate TTV with first-year retention to prove its impact.
Start simple. Even a 3-color RAG status (red/amber/green) based on 3 signals is better than nothing. Don't over-engineer it.
Build a real health score with 5–7 weighted inputs. The key is consistency: every CSM should assess health the same way.
Health scores should be automated and trigger workflows. Focus on predictive accuracy: does your score actually predict churn?
Don't focus here yet. Retain first, expand later. Premature expansion focus can damage customer trust.
Start tracking expansion informally. Identify which customers expanded and why, then look for patterns.
Expansion should be a formal CS metric with defined processes for identifying and advancing opportunities.
Useful as a quick pulse check, especially post-onboarding. Easy to implement.
Track CSAT at key lifecycle touchpoints. Use it as one input to health scores.
CSAT is useful but limited. Supplement with CES and outcome-based metrics.
Start with the basics: are customers logging in? How often? Even crude usage data is valuable.
Move beyond logins to feature-level adoption. Define what 'healthy adoption' looks like for each segment.
Adoption should be a leading indicator in your health score. Correlate adoption patterns with retention outcomes.
Define what 'first value' means for your product. Even a rough milestone is better than no milestone.
Track TTFV by segment and optimize your onboarding to hit it faster. Faster TTFV = better first-year retention.
TTFV should be a leading KPI with segment-specific targets. Automate milestone tracking and alert on delays.
First, define what 'complete onboarding' means. Document your onboarding steps before you can measure completion.
Track completion rate and identify where customers get stuck. This is the foundation for onboarding optimization.
Segment-specific completion targets should exist. Correlate completion rate with first-year retention to prove impact.
Track basic product usage first (logins, active users). Feature-level tracking can wait.
Define what 'active use' means for each key feature. Start tracking adoption depth for top-tier accounts.
Adoption depth should be a health score input and expansion indicator. Low depth = at-risk; high depth = expansion-ready.
Track basic MAU first. DAU/MAU ratio requires reliable daily usage data you may not have yet.
Start tracking DAU/MAU for your core product. Use it as a leading indicator alongside health scores.
DAU/MAU should be tracked by segment and product area. Use it to identify products/features with strongest stickiness.
Don’t focus on expansion yet. Retain first; pushing expansion before customers have value damages trust.
Begin tracking which accounts expanded and why. Look for patterns but don’t formalize expansion targets yet.
Expansion rate should be a formal metric. CS should have defined processes for identifying and qualifying expansion opportunities.
Not a CS metric at this stage. Focus on retention fundamentals before worrying about unit economics.
Awareness only. Understanding CAC payback helps CS leaders make the case for investment in customer retention.
Track CAC payback by segment. CS’s impact on retention directly affects payback: shorter churn means faster payback.
Not a priority yet. CSAT is easier to implement and gives you a basic sentiment baseline.
Consider adding CES to post-interaction surveys (after support tickets, after onboarding). It’s more actionable than CSAT.
CES should supplement CSAT as a health score input. High-effort experiences are strong churn predictors.
Track escalations informally. At this stage, every escalation is a learning opportunity about your product and processes.
Define what constitutes an escalation vs. a normal support issue. Start categorizing escalation types and root causes.
Escalation rate should be tracked formally with trend analysis. Use root cause data to drive product and process improvements.
Don't measure this yet. You're still deciding whether to use AI tools at all, and tracking a metric for a capability you haven't adopted creates noise.
Start tracking informally once you have a pilot (call summarization, health scoring copilot). Establishes a baseline before scaling.
Track formally. At this stage AI is embedded in multiple workflows, so you need to know the coverage and whether AI-assisted interventions produce better retention outcomes.
Not worth tracking formally. You likely have one or two CSMs and adoption is a personal choice at this stage. Focus on piloting one use case first.
Start measuring once you've rolled out an AI tool to the team. Low adoption signals the tool isn't solving a real pain or CSMs need more support.
High-priority input metric. If AI-assisted resolution rate is high but adoption rate is low, a small group is carrying the AI program; that's fragile and doesn't scale.
Too early to model reliably. You don't have enough retention history for CLV estimates to be accurate. Focus on keeping customers alive before projecting their value.
Useful as a strategic framing exercise, but don't optimize for it yet. Churn and expansion patterns aren't stable enough for CLV to drive decisions reliably.
Start building CLV models by segment. Use them to justify CS headcount investment, inform tiering decisions, and prioritize accounts for high-touch coverage.
Not a priority yet. You likely lack the survey volume for statistically meaningful results, and the real issue is whether customers are getting value at all. Start with CSAT at key touchpoints instead.
Start running quarterly relationship NPS surveys. The goal is building a baseline, not optimizing the score. Even basic detractor follow-up at this stage prevents preventable churn.
NPS should be a core CS metric. Segment by ARR tier, cohort, and product line. Use detractor workflows to protect renewal pipeline; use promoter identification to feed expansion and reference programs.
ARR visibility is useful for sizing the problem, but without GRR and logo retention under control, ARR growth will mask underlying churn risk. Focus on retention fundamentals before optimizing for ARR growth.
Begin tracking ARR by segment and cohort. CS should be able to report the ARR at risk and the ARR protected each quarter as a measure of team impact.
ARR is a core reporting metric for CS. Segment-level ARR trends, ARR at risk, and ARR influenced by CS motions should feed directly into QBRs and board reporting.
MRR reporting adds little value before your retention fundamentals are in place. Focus on understanding why customers churn before optimizing the revenue cadence you report at.
Start tracking MRR movement in four categories: new, expansion, contraction, and churned. Even basic monthly visibility into contraction MRR will surface at-risk accounts earlier than renewal-date monitoring alone.
MRR movement analysis should be a standard CS leadership metric. Contraction MRR trending upward is an early warning sign before logo churn hits. Use it to trigger proactive playbooks.
ACV awareness is useful for rough account prioritisation, but without a defined coverage model, it's hard to act on. Focus on understanding which accounts are at risk before optimising around deal size.
Use ACV to begin segmenting your book and calibrating CSM coverage ratios. High-ACV accounts typically warrant dedicated CSMs; lower ACV accounts may suit pooled or digital-led models.
ACV should inform your coverage model, QBR cadence, and escalation thresholds. Segment your CS team's activities and capacity planning directly against ACV tiers.
Skip this metric for now. CSQLs require a stable retention base and a working CS-to-Sales handoff, neither of which is in place at this stage. Focus on adoption and renewal first.
Begin informally tracking CS-sourced expansion ideas, but do not formalise targets. Use the data to identify which usage and relationship signals tend to precede genuine expansion.
CSQLs should be a formal metric with shared qualification criteria between CS and Sales, a documented handoff process, and weekly or monthly reporting on volume and conversion.
How to read the customer success metrics map
This map ranks the customer success metrics that matter at each maturity stage. Priority rises as a team matures: a metric that is low priority at Crawl is often high priority at Run, because the team has the foundations in place to act on it. Reading a row left to right shows how the importance of each metric shifts as the customer success function grows.
Two rules cut across every stage. Fewer metrics reviewed with discipline beat many tracked loosely, so start with the high-priority customer success metrics for your stage. And benchmarks must match your segment and ARR band. Each metric name links to its detail page, where you will find the formula, segment benchmarks, and common mistakes.
Frequently asked questions
Which customer success metrics matter most at each maturity stage?
At Crawl, focus on foundational retention and onboarding metrics: gross revenue retention, logo retention, and time to first value. At Walk, add net revenue retention and a composite health score. At Run, operationalize expansion metrics, leading indicators of churn, and predictive signals. The map above shows the priority of all 24 customer success metrics across the three stages.
When does net revenue retention become a priority?
Net revenue retention is low priority at Crawl, where gross retention matters more. It becomes a medium priority at Walk as you build the case for customer success as a revenue function, and a primary metric at Run, where expansion and risk management directly influence the number.
How should metric priorities change as a customer success team matures?
Priority should rise with capability. Early teams track fewer, foundational customer success metrics reliably before layering on health scoring and predictive analytics. Adding advanced metrics before the basics are clean measures the wrong thing first, which is why each metric carries a different priority at Crawl, Walk, and Run.